ABSTRACT
This paper analyzes the statistical impact of COVID-19 on the S&P500 and the CSI300 intraday momentum. This study employs an empirical method, that is, the intraday momentum method used in this research. Also, the predictability of timing conditional strategies is also used here to predict the intraday momentum of stock returns. In addition, this study aims to estimate and forecast the coefficients in the stock market pandemic crisis through a robust standard error approach. The empirical findings indicate that the intraday market behavior an unusual balanced;the volatility and trading volume imbalance and the return trends are losing overwhelmingly. The consequence is that the first half-hour return will forecast the last half-hour return of the S&P500, but during the pandemic shock, the last half-hour of both stock markets will not have a significant impact on intraday momentum. Additionally, market timing strategy analysis is a significant factor in the stock market because it shows the perfect trading time, decides investment opportunities and which stocks will perform well on this day. Besides, we also found that when the volatility and volume of the S&P500 are both at a high level, the first half-hour has been a positive impact, while at the low level, the CSI300 has a negative impact on the last half-hour. In addition, this shows that the optimistic effect and positive outlook of the stockholders for the S&P500 is in the first half-hours after weekend on Monday morning because market open during the weekend holiday, and the mentality of every stockholder's indicate the positive impression of the stock market.
ABSTRACT
With the recent outbreak of coronavirus disease 2019 (COVID-19), human life and the world economy have been severely affected, the propagation and scale of COVID-19 is top of mind for everyone. To reconstruct the development trend of COVID-19, we investigate the issue of the epidemic spreading process under vigorous non-pharmaceutical interventions. Here, an improved Susceptible-Exposed-Infectious-Recovered (SEIR) model with dynamic variables (i.e., health exposure individuals and close contacts) is proposed to predict the scale of COVID-19 and its dynamic evolution. We assume that the number of contacts and the reproduction number of COVID-19 changes dynamically over time. Then a gradient descent method is applied to estimate the effective reproduction number. We use the proposed model to reconstruct the dynamic transmission of COVID-19 in Chongqing between 14 January and 24 March 2020. The results show a similar development trend with a real-world epidemic. Our work has important implications when considering strategies for continuing surveillance and interventions to eventually contain outbreaks of COVID-19.